Search Results (28,608)

Anxiety and depression are common comorbidities in patients with chronic obstructive pulmonary disease (COPD), which can contribute to increased morbidity, reduced quality of life, and worse clinical outcomes. Nevertheless, these psychological conditions remain largely overlooked. This narrative review includes studies published between 1983 and 2025 to synthesise the current evidence on the risk factors, clinical impacts, and therapeutic strategies for these comorbidities. While the exact mechanisms leading to their increased prevalence are not fully understood, growing evidence implicates a combination of biological (e.g., systemic inflammation), social (e.g., isolation and stigma), and behavioural (e.g., smoking and inactivity) factors. Despite current guidelines recommending the identification and management of these comorbidities in COPD, they are not currently included in COPD assessments. Undetected and unmanaged anxiety and depression have serious consequences, including poor self-management, non-adherence to medications, increased risk of exacerbation and hospitalisations, and even mortality; thus, there is a need to incorporate screening as part of COPD assessments. There is robust evidence showing that pulmonary rehabilitation, a core non-pharmacological intervention, can improve mood symptoms, enhance functional capacity, and foster psychosocial resilience. Psychological therapies such as cognitive behavioural therapy (CBT), mindfulness-based approaches, and supportive counselling have also demonstrated value in reducing emotional distress and improving coping mechanisms. Pharmacological therapies, particularly selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine reuptake inhibitors (SNRIs), are commonly prescribed in moderate to severe cases or when non-pharmacological approaches prove inadequate. However, the evidence for their efficacy in COPD populations is mixed, with concerns about adverse respiratory outcomes and high discontinuation rates due to side effects. There are also barriers to optimal care, including underdiagnosis, a lack of screening protocols, limited provider training, stigma, and fragmented multidisciplinary coordination. A multidisciplinary, biopsychosocial approach is essential to ensure early identification, integrated care, and improved outcomes for patients with COPD. Full article

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for irregular TEC variations. To address this limitation, we enhance SA-ConvLSTM by incorporating deformable convolution, proposing SA-DConvLSTM. This achieves adaptive spatial feature extraction through learnable offsets in convolutional kernels. Building on this improvement, we design ED-SA-DConvLSTM, a TEC spatiotemporal prediction model based on an encoder–decoder architecture with SA-DConvLSTM as its fundamental block. Firstly, the effectiveness of the model improvement was verified through an ablation experiment. Subsequently, a comprehensive quantitative comparison was conducted between ED-SA-DConvLSTM and baseline models (C1PG, ConvLSTM, and ConvGRU) in the region of 12.5° S–87.5° N and 25° E–180° E. The experimental results showed that the ED-SA-DConvLSTM exhibited superior performance compared to C1PG, ConvGRU, and ConvLSTM, with prediction accuracy improvements of 10.27%, 7.65%, and 7.16% during high solar activity and 11.46%, 4.75%, and 4.06% during low solar activity, respectively. To further evaluate model performance under extreme conditions, we tested the ED-SA-DConvLSTM during four geomagnetic storms. The results showed that the proportion of its superiority over the baseline models exceeded 58%. Full article

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is designed with a simple structure, consisting of two main subsystems: a meta-learning-triggered mechanism-based physics-informed neural network (MLTM-PINN) for plant identification and a self-tuning neural network controller (STNNC). This structure, featuring the triggered mechanism, facilitates a balance between high controllability and control efficiency. The MLTM-PINN incorporates the following: (I) a single self-supervised physics-informed neural network (PINN) without the need for labelled data, enabling online learning in control; (II) a meta-learning-triggered mechanism to ensure consistent control performance; (III) transfer learning combined with meta-learning for finely tailored initialization and quick adaptation to input changes. To resolve the conflict between streamlining the AICS’s structure and enhancing its controllability, the STNNC functionally integrates the nonlinear controller and adaptation laws from the MRAC system. Three STNNC design scenarios are tested with transfer learning and/or hyperparameter optimization (HPO) using a Gaussian process tailored for Bayesian optimization (GP-BO): (scenario 1) applying transfer learning in the absence of the HPO; (scenario 2) optimizing a learning rate in combination with transfer learning; and (scenario 3) optimizing both a learning rate and the number of neurons in hidden layers without applying transfer learning. Unlike scenario 1, no quick adaptation effect in the MLTM-PINN is observed in the other scenarios, as these struggle with the issue of dynamic input evolution due to the HPO-based STNNC design. Scenario 2 demonstrates the best synergy in controllability (best control response) and efficiency (minimal activation frequency of meta-learning and fewer trials for the HPO) in control. Full article

Nonlinear wind-induced vibrations and coupled static–dynamic instabilities pose significant challenges for long-span suspension bridges, especially under large-amplitude and high-angle-of-attack conditions. However, existing studies have yet to fully capture the mechanisms behind large-amplitude torsional flutter. To address this, wind tunnel experiments were performed on H-shaped bluff sections and closed box girders using a high-precision five-component piezoelectric balance combined with a custom support system. Complementing these experiments, a finite element time-domain simulation framework was developed, incorporating experimentally derived nonlinear flutter derivatives. Validation was achieved through aeroelastic testing of a 1:110-scale model of the original Tacoma Narrows Bridge and corresponding numerical simulations. The results revealed Hopf bifurcation phenomena in H-shaped bluff sections, indicated by amplitude-dependent flutter derivatives and equivalent damping coefficients. The simulation results showed less than a 10% deviation from experimental and historical wind speed–amplitude data, confirming the model’s accuracy. Failure analysis identified suspenders as the critical failure components in the Tacoma collapse. This work develops a comprehensive performance-based design framework that improves the safety, robustness, and resilience of long-span suspension bridges against complex nonlinear aerodynamic effects while enabling cost-effective, targeted reinforcement strategies to advance modern bridge engineering. Full article

Background: Varicella remains a prevalent vaccine-preventable disease, but breakthrough infections are increasingly reported. However, long-term, population-based studies investigating the temporal and demographic characteristics of breakthrough varicella remain limited. Methods: This retrospective study analyzed surveillance data from Jinhua City, China, from 2016 to 2024. Varicella case records were obtained from the China Information System for Disease Control and Prevention (CISDCP), while vaccination data were retrieved from the Zhejiang Provincial Immunization Program Information System (ISIS). Breakthrough cases were defined as infections occurring more than 42 days after administration of the varicella vaccine. Differences in breakthrough interval were analyzed across subgroups defined by dose, sex, age, population category, and vaccination type. A bivariate cubic regression model was used to assess the combined effect of initial vaccination age and dose interval on the breakthrough interval. Results: Among 28,778 reported varicella cases, 7373 (25.62%) were classified as breakthrough infections, with a significant upward trend over the 9-year period (p < 0.001). Most cases occurred in school-aged children, especially those aged 6–15 years. One-dose recipients consistently showed shorter breakthrough intervals than two-dose recipients (M = 62.10 vs. 120.10 months, p < 0.001). Breakthrough intervals also differed significantly by sex, age group, population category, and vaccination type (p < 0.05). Regression analysis revealed a negative correlation between the initial vaccination age, the dose interval, and the breakthrough interval (R² = 0.964, p < 0.001), with earlier and closely spaced vaccinations associated with longer protection. Conclusions: The present study demonstrates that a two-dose varicella vaccination schedule, when initiated at an earlier age and administered with a shorter interval between doses, provides more robust and longer-lasting protection. These results offer strong support for incorporating varicella vaccination into China’s National Immunization Program to enhance vaccine coverage and reduce the public health burden associated with breakthrough infections. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

In this paper, we propose a sparse distributionally robust optimization (DRO) model incorporating the Conditional Value-at-Risk (CVaR) measure to control tail risks in uncertain environments. The model utilizes sparsity to reduce transaction costs and enhance operational efficiency. We reformulate the problem as a Min-Max-Min optimization and convert it into an equivalent non-smooth minimization problem. To address this computational challenge, we develop an approximate discretization (AD) scheme for the underlying continuous random vector and prove its convergence to the original non-smooth formulation under mild conditions. The resulting problem can be efficiently solved using a subgradient method. While our analysis focuses on CVaR penalty, this approach is applicable to a broader class of non-smooth convex regularizers. The experimental results on the portfolio selection problem confirm the effectiveness and scalability of the proposed AD algorithm. Full article

Background: Multi-epitope vaccines have become the preferred strategy for protection against infectious diseases by integrating multiple MHC-restricted T-cell and B-cell epitopes that elicit both humoral and cellular immune responses against pathogens. Computational methods address various aspects independently, yet their orchestration is technically challenging, as most bioinformatics tools are accessible through heterogeneous interfaces and lack interoperability features. The present work proposes a novel framework for rationalized multi-epitope vaccine design that streamlines end-to-end analyses through an integrated web-based environment. Results: VaccineDesigner is a comprehensive web-based framework that streamlines the design of protective epitope-based vaccines by seamlessly integrating computational methods for B-cell, CTL, and HTL epitope prediction. VaccineDesigner incorporates single-epitope prediction and evaluation as well as additional analyses, such as multi-epitope vaccine generation, estimation of population coverage, molecular mimicry, and proteasome cleavage. The functionalities are transparently integrated into a modular architecture, providing a single access point for rationalized, multi-epitope vaccine generation in a time- and cost-effective manner. Conclusions: VaccineDesigner is a web-based tool that identifies and evaluates candidate B-cell, CTL, and HTL epitopes and constructs a library of multi-epitope vaccines that combine strong immunogenic responses, safety, and broad population coverage. The source code is available under the academic license and freely accessible. Full article

The growing demand for innovation and the use of diverse materials in cementitious composites necessitate predictive models that account for material variability. Numerical, code-based, and machine learning (ML) models have been developed to predict various concrete properties. However, their accuracy is significantly influenced by factors such as mix design, composition, intrinsic properties, and external conditions. Developing robust models that integrate these variables is essential for improving predictive accuracy and optimizing material performance. This paper presents a comprehensive review of numerical, code-based, and ML modelling techniques for predicting both fresh and long-term concrete properties. Since both numerical and ML models rely on experimental data—either to determine coefficients in numerical approaches or to train ML models—data gathering, preprocessing, and handling are crucial for model performance. Previous studies indicated that data variability significantly impacts accuracy, emphasizing the importance of effective preprocessing. While larger datasets generally improve reliability, some models achieve high accuracy even with very limited data. This review not only demonstrates the superior performance of ML models over traditional numerical approaches but also highlights the relative effectiveness of different ML algorithms based on reported accuracy metrics. ML-based approaches, including both ensemble and non-ensemble models, have exhibited strong predictive capabilities across a wide range of concrete property categories. In contrast, traditional numerical models often yield lower accuracy, although modified versions that incorporate additional parameters have shown improved performance. Furthermore, the integration of optimization algorithms and interpretability tools enhances both predictive reliability and model transparency—critical aspects that are often overlooked. Full article

Background: Relapsed/refractory acute myeloid leukemia (R/R AML) remains a therapeutic challenge due to disease heterogeneity, resistance mechanisms, and poor tolerability to intensive regimens. Venetoclax (VEN), a BCL-2 inhibitor, has shown promise in combination with hypomethylating agents (HMAs), but data on response timing in the R/R setting are limited. The aim of this study was to assess the efficacy, safety, and kinetics of response to HMA-VEN therapy in a real-world cohort of R/R AML patients, with particular focus on early versus late responders. Methods: This prospective single-center study included 33 adult patients with R/R AML treated with VEN plus either azacitidine (AZA) or decitabine (DEC) from 2018 to 2021. The primary endpoint was the composite complete remission (cCR) rate and the rate of early and late response, respectively, occurring within two cycles of therapy or later; secondary endpoints included overall survival (OS), relapse-free survival (RFS), time to relapse (TTR), and safety. Results: The cCR was 58%, with complete remission (CR) or CR with incomplete recovery (CRi) achieved in 52% of patients. Median OS was 9 months. No significant differences in OS or TTR were observed between early (≤2 cycles) and late (>2 cycles) responders. Eight responders (42%) underwent allogeneic hematopoietic stem cell transplantation (HSCT), with comparable transplant rates in both groups of responders. Toxicity was manageable. Grade 3–4 neutropenia occurred in all patients, and febrile neutropenia occurred in 44% of patients. An Eastern Cooperative Oncology Group (ECOG) score >2 was associated with inferior response and shorter treatment duration. Conclusions: HMA-VEN therapy is effective and safe in R/R AML, including for patients with delayed responses. The absence of a prognostic disadvantage for late responders supports flexible treatment schedules and suggests that the continuation of therapy may be beneficial even without early blast clearance. Tailored approaches based on performance status and comorbidities are warranted, and future studies should incorporate minimal residual disease (MRD)-based monitoring to refine response assessment. Full article

Pain management in multiple trauma patients presents a complex clinical challenge due to competing priorities such as hemodynamic instability, polypharmacy, coagulopathy, and the urgency of life-saving interventions. In this context, peripheral nerve blocks (PNBs) are increasingly recognized as a valuable asset for their role in managing pain in patients with multiple traumatic injuries. By reducing reliance on systemic opioids, PNBs support effective pain control and facilitate early mobilization, aligning with enhanced recovery principles. This narrative review summarizes current evidence on the use of PNBs in the context of polytrauma, focusing on their analgesic efficacy, integration within multimodal analgesia protocols, and contribution to improved functional outcomes. Despite these advantages, clinical application is limited by specific concerns, including the potential to mask compartment syndrome, the risk of nerve injury or local anesthetic systemic toxicity (LAST), and logistical barriers in acute trauma settings. Emerging directions in the field include the refinement of ultrasound-guided PNB techniques, the expanded use of continuous catheter systems, and the incorporation of fascial plane blocks for anatomically complex or multisite trauma. Parallel efforts are focusing on the development of decision-making algorithms, improved risk stratification tools, and integration into multimodal analgesic pathways. There is also growing emphasis on standardized clinical protocols, simulation-based training, and patient education to enhance safety and consistency in practice. As evidence continues to evolve, the long-term impact of PNBs on functional recovery, quality of life, and healthcare utilization must be further explored. With thoughtful implementation, structured training, and institutional support, PNBs may evolve into a cornerstone of modern trauma analgesia. Full article

This study focuses on realistic modeling of forming load and microstructural evolution during hot metal extrusion, emphasizing the effects of friction models and hydraulic press behavior. Rather than merely predicting load magnitudes, the objective is to replicate actual press operation by integrating a load limit response into finite element modeling (FEM). By applying Coulomb and shear friction models under both constant and hydraulically controlled press conditions, the resulting impact on grain size evolution during deformation is examined. The hydraulic press simulation features a maximum load threshold that dynamically reduces die velocity once the limit is reached, unlike constant presses that sustain velocity regardless of load. P91 steel is used as the material system, and the predicted grain size is validated against experimentally measured data. Incorporating hydraulic control into FEM improves the representativeness of simulation results for industrial-scale extrusion, enhancing microstructural prediction accuracy, and ensuring forming process reliability. Full article

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin. Full article